Computational Structure Prediction for Interfaces: What is Currently Possible?

Studying the electronic structure of organic monolayers on metals requires knowledge about their atomistic structure. Such monolayers display rich polymorphism arising from diverse molecular arrangements. The large number of possible orientations and motifs poses a big challenge for determining the different polymorphs from first principles. To meet this challenge, we develop SAMPLE[1], which employs coarse-grained modeling and machine learning to efficiently map the minima of commensurate organic adlayers. With a few hundred DFT calculations as input, we use Bayesian linear regression to determine the parameters of a physically motivated energy model. These parameters yield meaningful physical insight and allow predicting adsorption energies for millions of possible polymorphs with high accuracy.
Beyond that, we continuously push the boundaries of surface structure search, with three noteworthy developments: i) predicting the second adlayer pursuing the goal of studying thin film properties; ii) generalizing SAMPLE to investigate incommensurate adlayers; Iii) employing feature recognition to reveal hidden relationships between the interface properties.

[1] Hörmann et al., Comput. Phys. Commun. 244, 143–155, 2019